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第53行：
第53行： − How you were parented <math> \to </math> confidence in own parenting abilities.+ 第77行：
第77行： − How you were parented <math> \to </math> Feelings of competence and self-esteem.+ 第167行：
第167行： − + 第187行：
第187行： − + 第396行：
第396行： − − [ math > TE = e [ y (1)-y (0)] 第408行：
第406行： − − [ math > CDE (m) = e [ y (1，m)-y (0，m)] </math > 第420行：
第416行： − − [ math > NDE = e [ y (1，m (0)-y (0，m (0)] 第432行：
第426行： − − [ math > NIE = e [ y (0，m (1))-y (0，m (0)] − 第489行：
第480行： − <math>TE = NDE - NIE_r </math>+ − − <math>TE = NDE - NIE_r </math> 第546行：
第535行： − − 《数学》 − − − 开始{ align } − − − − − 等于 c + AB 第819行：
第798行： − <math> \frac{NIE}{TE} = \frac{b_1 c_2}{c_1 + b_0 c_3 + b_1 (c_2 + c_3)}, </math>+ − 1 + b _ 0 c _ 3 + b _ 1(c _ 2 + c _ 3) ，</math > 第835行：
第813行： − <math> 1- \frac{NDE}{TE} = \frac{b_1 (c_2 +c_3)}{c_1 + b_0c_3 + b_1 (c_2 + c_3)}. </math>+ − − 1-frac { NDE }{ TE } = frac { b _ 1(c _ 2 + c _ 3)}{ c _ 1 + b _ 0c _ 3 + b _ 1(c _ 2 + c _ 3)}.数学 第883行：
第859行： − + − − | last = Preacher − − | last = Preacher − − * To establish overall moderation, the ''β''<sub>43</sub> regression weight must be significant (first step for establishing mediated moderation). − − | first = Kristopher J. − 第一，克里斯托弗 j。+ − − | last2 = Hayes − − 2 = Hayes − − − − | first2 = Andrew F. − − 2 = Andrew f. − − | title = SPSS and SAS procedures for estimating indirect effects in simple mediation models − − | title = 用于估计简单调解模型中的间接影响的 SPSS 和 SAS 程序 − − | journal = Behavior Research Methods, Instruments, and Computers − − | volume = 36 − − 36 − − − − | issue = 4 − − 第四期 − − | pages = 717–731 − − | 页数 = 717-731 − − | url = http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html − − Http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html − − | year = 2004 − − 2004年 − − | doi = 10.3758/BF03206553 − − | doi = 10.3758/BF03206553 − − | pmid = 15641418 − − 15641418 − − − − | doi-access = free − − 免费访问 − − − }} − − }} − − − − | last = Preacher − − | last = Preacher + − − | first = Kristopher J. − − 第一，克里斯托弗 j。 − − Mediation analysis quantifies the − − | last2 = Hayes − − 2 = Hayes − − extent to which a variable participates in the transmittance − − | first2 = Andrew F. − − 2 = Andrew f. − − of change from a cause to its effect. It is inherently a causal − − | title = Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models − − | title = 评估和比较多重介质模型中间接影响的渐近和重采样策略 − − | journal = Behavior Research Methods − − 行为研究方法 − − | volume = 40 − − 40 − − | issue = 3 − − 第三期 − − | pages = 879–891 − − 879-891 − − | url = http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html − − Http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html − − | year = 2008 − − 2008年
无编辑摘要
: Independent variable <math> \to </math> dependent variable
: Independent variable <math> \to </math> dependent variable
How you were parented <math> \to </math> confidence in own parenting abilities.
你是如何培养孩子对自己养育能力的信心的。
你是如何培养孩子对自己养育能力的信心的。
:Regress the mediator on the independent variable to confirm that the independent variable is a significant predictor of the mediator. If the mediator is not associated with the independent variable, then it couldn’t possibly mediate anything.
:Regress the mediator on the independent variable to confirm that the independent variable is a significant predictor of the mediator. If the mediator is not associated with the independent variable, then it couldn’t possibly mediate anything.
How you were parented <math> \to </math> Feelings of competence and self-esteem.
你是如何培养自己的能力和自尊心的。
你是如何培养自己的能力和自尊心的。
: How you were parented <math> \to </math> confidence in own parenting abilities.
:How you were parented <math> \to </math> confidence in own parenting abilities.
: How you were parented <math> \to </math> Feelings of competence and self-esteem.
:How you were parented <math> \to </math> Feelings of competence and self-esteem.
(1) Experimental-causal-chain design
(1) Experimental-causal-chain design
<math>TE = E [Y(1) - Y(0)] </math>
<math>TE = E [Y(1) - Y(0)] </math>
<math> CDE(m) = E [Y(1,m) - Y(0,m) ] </math>
<math> CDE(m) = E [Y(1,m) - Y(0,m) ] </math>
<math>NDE = E [Y(1,M(0)) - Y(0,M(0))] </math>
<math>NDE = E [Y(1,M(0)) - Y(0,M(0))] </math>
<math> NIE = E [Y(0,M(1)) - Y(0,M(0))] </math>
<math> NIE = E [Y(0,M(1)) - Y(0,M(0))] </math>
<math>TE = NDE - NIE_r </math>
Experimental approaches to mediation must be carried out with caution. First, it is important to have strong theoretical support for the exploratory investigation of a potential mediating variable.
Experimental approaches to mediation must be carried out with caution. First, it is important to have strong theoretical support for the exploratory investigation of a potential mediating variable.
<math>
<math>
(1) Confounding:
(1) Confounding:
\begin{align}
\begin{align}
TE & = C + AB \\
TE & = C + AB \\
:Another model that is often tested is one in which competing variables in the model are alternative potential mediators or an unmeasured cause of the dependent variable. An additional variable in a [[causal model]] may obscure or confound the relationship between the independent and dependent variables. Potential confounders are variables that may have a causal impact on both the independent variable and dependent variable. They include common sources of measurement error (as discussed above) as well as other influences shared by both the independent and dependent variables.
:Another model that is often tested is one in which competing variables in the model are alternative potential mediators or an unmeasured cause of the dependent variable. An additional variable in a [[causal model]] may obscure or confound the relationship between the independent and dependent variables. Potential confounders are variables that may have a causal impact on both the independent variable and dependent variable. They include common sources of measurement error (as discussed above) as well as other influences shared by both the independent and dependent variables.
<math> \frac{NIE}{TE} = \frac{b_1 c_2}{c_1 + b_0 c_3 + b_1 (c_2 + c_3)}, </math>
In order to establish mediated moderation, one must first establish [[Moderation (statistics)|moderation]], meaning that the direction and/or the strength of the relationship between the independent and dependent variables (path ''C'') differs depending on the level of a third variable (the moderator variable). Researchers next look for the presence of mediated moderation when they have a theoretical reason to believe that there is a fourth variable that acts as the mechanism or process that causes the relationship between the independent variable and the moderator (path ''A'') or between the moderator and the dependent variable (path ''C'').
In order to establish mediated moderation, one must first establish [[Moderation (statistics)|moderation]], meaning that the direction and/or the strength of the relationship between the independent and dependent variables (path ''C'') differs depending on the level of a third variable (the moderator variable). Researchers next look for the presence of mediated moderation when they have a theoretical reason to believe that there is a fourth variable that acts as the mechanism or process that causes the relationship between the independent variable and the moderator (path ''A'') or between the moderator and the dependent variable (path ''C'').
<math> 1- \frac{NDE}{TE} = \frac{b_1 (c_2 +c_3)}{c_1 + b_0c_3 + b_1 (c_2 + c_3)}. </math>
The following is a published example of mediated moderation in psychological research.<ref>{{cite journal | last1 = Smeesters | first1 = D. | last2 = Warlop | first2 = L. | last3 = Avermaet | first3 = E. V. | last4 = Corneille | first4 = O. | last5 = Yzerbyt | first5 = V. | year = 2003 | title = Do not prime hawks with doves: The interplay of construct activation and consistency of social value orientation on cooperative behavior | journal = Journal of Personality and Social Psychology | volume = 84 | issue = 5| pages = 972–987 | doi = 10.1037/0022-3514.84.5.972 | pmid = 12757142 }}</ref>
The following is a published example of mediated moderation in psychological research.<ref>{{cite journal | last1 = Smeesters | first1 = D. | last2 = Warlop | first2 = L. | last3 = Avermaet | first3 = E. V. | last4 = Corneille | first4 = O. | last5 = Yzerbyt | first5 = V. | year = 2003 | title = Do not prime hawks with doves: The interplay of construct activation and consistency of social value orientation on cooperative behavior | journal = Journal of Personality and Social Psychology | volume = 84 | issue = 5| pages = 972–987 | doi = 10.1037/0022-3514.84.5.972 | pmid = 12757142 }}</ref>
'''Step 1''': Moderation of the relationship between the independent variable (X) and the dependent variable (Y), also called the overall treatment effect (path ''C'' in the diagram).
'''Step 1''': Moderation of the relationship between the independent variable (X) and the dependent variable (Y), also called the overall treatment effect (path ''C'' in the diagram).
: <math>Y=\beta_{40} +\beta_{41}X +\beta_{42}Mo +\beta_{43}XMo + \varepsilon_4</math>
:<math>Y=\beta_{40} +\beta_{41}X +\beta_{42}Mo +\beta_{43}XMo + \varepsilon_4</math>
* To establish overall moderation, the ''β''<sub>43</sub> regression weight must be significant (first step for establishing mediated moderation)
* Establishing moderated mediation requires that there be no moderation effect, so the ''β''<sub>43</sub> regression weight must not be significant.
* Establishing moderated mediation requires that there be no moderation effect, so the ''β''<sub>43</sub> regression weight must not be significant.
'''Step 2''': Moderation of the relationship between the independent variable and the mediator (path ''A'').
'''Step 2''': Moderation of the relationship between the independent variable and the mediator (path ''A'').
: <math>Me=\beta_{50} +\beta_{51}X +\beta_{52}Mo +\beta_{53}XMo + \varepsilon_5</math>
: <math>Me=\beta_{50} +\beta_{51}X +\beta_{52}Mo +\beta_{53}XMo + \varepsilon_5</math>
行为研究方法，仪器和计算机
行为研究方法，仪器和计算机
* If the ''β''<sub>53</sub> regression weight is significant, the moderator affects the relationship between the independent variable and the mediator.
* If the ''β''<sub>53</sub> regression weight is significant, the moderator affects the relationship between the independent variable and the mediator.
'''Step 3''': Moderation of both the relationship between the independent and dependent variables (path ''A'') and the relationship between the mediator and the dependent variable (path ''B'').
'''Step 3''': Moderation of both the relationship between the independent and dependent variables (path ''A'') and the relationship between the mediator and the dependent variable (path ''B'').
: <math>Y=\beta_{60} +\beta_{61}X +\beta_{62}Mo +\beta_{63}XMo +\beta_{64}Me +\beta_{65}MeMo + \varepsilon_6</math>
: <math>Y=\beta_{60} +\beta_{61}X +\beta_{62}Mo +\beta_{63}XMo +\beta_{64}Me +\beta_{65}MeMo + \varepsilon_6</math>
* If both ''β''<sub>53</sub> in step 2 and ''β''<sub>63</sub> in step 3 are significant, the moderator affects the relationship between the independent variable and the mediator (path ''A'').
* If both ''β''<sub>53</sub> in step 2 and ''β''<sub>63</sub> in step 3 are significant, the moderator affects the relationship between the independent variable and the mediator (path ''A'').
* If both ''β''<sub>53</sub> in step 2 and ''β<sub>65</sub>'' in step 3 are significant, the moderator affects the relationship between the mediator and the dependent variable (path ''B'').
* If both ''β''<sub>53</sub> in step 2 and ''β<sub>65</sub>'' in step 3 are significant, the moderator affects the relationship between the mediator and the dependent variable (path ''B'').
* Either or both of the conditions above may be true.
* Either or both of the conditions above may be true.
==Causal mediation analysis==
==Causal mediation analysis==
===Fixing versus conditioning===
===Fixing versus conditioning===
Mediation analysis quantifies the extent to which a variable participates in the transmittance of change from a cause to its effect. It is inherently a causal
notion, hence it cannot be defined in statistical terms. Traditionally,
notion, hence it cannot be defined in statistical terms. Traditionally,
however, the bulk of mediation analysis has been conducted
however, the bulk of mediation analysis has been conducted
within the confines of linear regression, with statistical
within the confines of linear regression, with statistical
terminology masking the causal character of the
terminology masking the causal character of the
relationships involved. This led to difficulties,
relationships involved. This led to difficulties,
biases, and limitations that have been alleviated by
biases, and limitations that have been alleviated by
modern methods of causal analysis, based on causal diagrams
modern methods of causal analysis, based on causal diagrams